Liver CT Image Classification of Colorectal Cancer Patients Based on Decision Tree Model

Objective: To evaluate the application of decision tree model based on data mining in liver CT image classification in patients with colorectal cancer. Methods: 60 patients with colorectal cancer were enrolled in this study, including 20 cases with liver metastasis, 20 cases with simple hepatic cyst and 20 cases with normal liver respectively. All patients underwent CT contrast enhancement examination. The texture features of liver CT images of the 60 cases were extracted by using gray histogram, gray level co-occurrence matrix and image transform. Then use the naive Bias classifier and decision tree model to classify the images. Eventually compare the final classification results with clinical fact and verify the validity of the two classification models with ten-fold cross validation method. Results: For the evaluation of liver lesions, the classification of decision tree based on data mining had much higher accuracy than that of the naive Bias classifier(accuracy 96.7% vs 76.7% P＜ 0.05; Kappa 0.95 vs 0.65, P＜ 0.05). Conclusion: The decision tree data based on mining model not only can judge whether the liver has related lesions in colorectal cancer patients, but also can automatically identify the liver metastasis and simple hepatic cysts based on the basic characteristics of the image, which may provide the reference information and effective way of computer aided diagnosis and treatment of diseases for the future.